Methods for indexing and retrieving information

ABSTRACT

A preferred method for providing an indexing methodology, an index table and method for retrieving information are disclosed. In a preferred method, an association between a plurality of word elements from a first data corpus such as a query is identified. Then, a preferred index table comprising additional information for identifying the associations between the several word elements of other data corpuses such as a data source is also disclosed, which in conjunction lead to retrieval of irrelevance-free information.

RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplication Ser. No. 61/210,396 filed 2009 Mar. 18 by the presentinventor.

BACKGROUND

1. Field of Invention

The present invention relates generally to a method for retrievinginformation. More particularly, a novel method(s) for retrievinginformation implementing indexing information identifying an associationbetween word elements.

2. Description of Related Art

The Revolution of the computer and the digital age are accountable for aseries of inventions, communications and the transfer of knowledgeincluding the storage of large amounts of valuable data upon whichhumanity sustains its progress. Many new scientific disciplines likeComputational Linguistics and Natural Language Processing are born tostudy and understand some of the communication mediums such as naturallanguages. Regarded Intranets and Internet are built to distribute thevaluable communication and knowledge to serve the specific informationneeds of millions of people every day. In particular, search engines arein charge of retrieving and delivering millions of documents to fulfillthe specific needs of millions of people. However, current searchtechnologies fail to effectively retrieve the information in a specificmanner requiring its users to spend time and effort reading throughlarge collections of text to find their particular or specificinformation needs. For example, a user looking to buy “red boots” maysimply enter in the search engine the words “red boots.” The searchengine then retrieves every document comprising the words “red” and“boots” producing data such as “red hat and yellow boots” which byhaving nothing to do with “red boots” it fails to serve or fulfills thespecific wants of its user. As a result, users are forced to usevaluable time and concentration in the efforts of focusing to sort andselect through large quantities of relevant and irrelevant data whichultimately contributes to user confusion, frustration, discourage andloss of concentration.

In view of the present shortcomings, the present invention distinguishesover the prior art by providing heretofore a more compelling andeffective method for retrieving specific information to allow searchengines and other application the ability to remove irrelevant data fromtheir results for better serving the needs of their users whileproviding additional unknown, unsolved and unrecognized advantages asdescribed in the following summary.

SUMMARY OF THE INVENTION

The present invention teaches certain benefits in use and constructionwhich give rise to the objectives and advantages described below. Themethods and systems embodied by the present invention overcome thelimitations and shortcomings encountered when retrieving information.The method(s) permits, through the use of a more compelling form ofindexing methodology, a more accurate and precise form of massiveinformation retrieval, which by implementing of associations betweenword elements, is capable of eliminating all the irrational andnonsensical data from user results.

OBJECTS AND ADVANTAGES

A primary objective inherent in the above described methods of use is toprovide several methods and systems to index and identify the desiredassociations between words, thus allowing the method and systems toeffectively reduce or remove the retrieval of irrelevant data not taughtby the prior arts and further advantages and objectives not taught bythe prior art. Accordingly, several objects and advantages of theinvention are:

Another objective is to save user time by providing only conceptuallymatching data.

A further objective is to decrease the amount of effort implemented byusers discriminating or sorting between relevant and irrelevant data.

A further objective is to improve the quality and quantity of results.

A further objective is to permit machines and application the ability ofhandling natural language more efficiently.

A further objective is to improve the ability of portable devices tomanipulate natural language.

Another further objective is to permit the unification of the world'sknowledge regardless of language and/or grammar.

Another further objective is to permit the retrieval of non-irrelevantdata from large collections of information storage.

Other features and advantages of the described methods of use willbecome apparent from the following more detailed description, taken inconjunction with the accompanying drawings which illustrate, by way ofexample, the principles of the presently described apparatus and methodof its use.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate examples of at least one of thebest mode embodiments of the present method and methods of use. In suchdrawings:

FIG. 1 illustrates several exemplary non-limiting diagrams of some stepsof the inventive method identifying and/or numbering the relationshipsbetween the elements of several exemplary data corpuses;

FIG. 2A is a non-limiting exemplary diagram of some steps of theinventive method displaying an index table, here introduced asAssociative Index Table, which exploits the new idea of indexing theassociation between the different word elements;

FIG. 2B is another non-limiting exemplary diagram of some steps of theinventive method displaying another type of Associative Index Table,which in contrast to the index table from FIG. 2A, this type of tablealso uses the concept of sorting alphabetically the word elements fromeach association;

FIG. 3A is a non-limiting exemplary block diagram of some significantsteps the inventive method handling a query and an index table foridentifying information that matches the query and therefore needs to beretrieved;

FIG. 3B is another non-limiting exemplary block diagram of somesignificant steps the inventive method handling a query with severalassociations and an index table for identifying information that matchesthe associations in the query for retrieving matching information;

FIG. 3C is another non-limiting exemplary block diagram of a variationof some of the most significant steps the inventive method discussed inFIG. 3B manipulating group identifiers;

FIG. 3D is another non-limiting exemplary block diagram of a variationof some of the most significant steps the inventive method discussed inFIG. 3B manipulating other word elements such as eeggis;

FIG. 3E is another non-limiting exemplary block diagram of a variationof some of the most significant steps the inventive method, this timeimplementing an associative index table similar to that exemplified inFIG. 2B which uses alphabetically sorted indices;

FIG. 3F is yet another non-limiting exemplary block diagram of somesignificant steps the inventive method handling a query with severalassociations and an associative index table for identifying informationthat matches the associations of the said query for retrieving matchinginformation;

FIG. 3G is yet another non-limiting exemplary block diagram of avariation of some of the most significant steps the inventive methoddiscussed in FIG. 3B this time manipulating other word elements such asgroup identifiers instead of words;

FIG. 3H is yet another non-limiting exemplary block diagram of avariation of some of the most significant steps the inventive methoddiscussed in FIG. 3B this time manipulating other word elements such aseeggis instead of words;

FIG. 3I is yet another non-limiting exemplary diagram of a variation ofsome of the most significant steps the inventive method this timeinvolving the word elements from associations in no particular order;

FIG. 3J is yet another non-limiting exemplary block diagram of avariation of some of the most significant steps the inventive methodimplementing a combination of index tables separated from the documentsown set of relations;

FIG. 4A is a non-limiting block diagram of some of the steps of theinventive method discussed in FIG. 1 and FIG. 2A for producing orproviding an indexing table for finding and/or comparing and/orretrieving matching information;

FIG. 4B is a non-limiting block diagram of some of the steps of theinventive method discussed in FIG. 1 and FIG. 2B for producing orproviding an indexing table for finding and/or comparing and/orretrieving matching information;

FIG. 5A is a non-limiting block diagram of some of the main steps of theinventive method displayed in FIGS. 3A, 3B, 3C and 3D for retrievinginformation;

FIG. 5B is yet another non-limiting block diagram of some of the mainsteps of the inventive method displayed in FIGS. 3E, 3F, 3G and 3H forretrieving information;

FIG. 6 is a non-limiting block diagram of the some steps of theinventive method exploring a more general view of the steps mentioned inFIG. 5A and FIG. 5B.

DETAILED DESCRIPTION

The above described drawing figures illustrate the described methods anduse in at least one of its preferred, best mode embodiment, which arefurther defined in detail in the following description. Those havingordinary skill in the art may be able to make alterations andmodifications from what is described herein without departing from itsspirit and scope. Therefore, it must be understood that what isillustrated is set forth only for the purposes of example and that itshould not be taken as a limitation in the scope of the present systemand method of use.

FIG. 1 illustrates several exemplary non-limiting diagrams of some stepsof the inventive method identifying and/or numbering the relationshipsbetween the elements of several exemplary data corpuses that were foundor attained by an Associative Analysis Protocol such as CIRN.Noteworthy, CIRN discovers and forms associations between the differentword elements of a given and/or analyzed data corpus, such asassociating the nouns with their verbs, etc. The First Data Corpus 1010(FIG. 1) or sentence “red boots and black hats” is displayed with itscorresponding First Table of Relations 1015 (FIG. 1) which contains therelationships found, formed or desired between the words or the elementsof the said first sentence. For example, in the First Table ofRelations, the top row displays the word “red” (under column Word 1)next to the word “boots” (under column Word2) along with theirassociation number or “1” (under column Association Number or “Assoc.No.” for short). In the bottom row, the word “black” (under columnWord1), the word “hats” (under column Word2) and their associationnumber “2” (under column Assoc. No.) are all displayed together. In suchfashion, each of the two associations (“red—boots” and “black—hats”)that were formed or found in the first sentence is uniquely identified,differentiated and/or numbered. The Second Data Corpus 1020 (FIG. 1) or“Mary ran quickly” illustrates its corresponding table of relationshipsor Second Table of Relations 1025 (FIG. 1). In this table, the top rowassociates the word “Mary,” the word “ran” and their association number“15.” In similar fashion, the bottom row associates the word “ran,” theword “quickly” and their association number “16.” As a result, each ofthe relations formed/found in the second sentence is uniquely numberedor identified. The Third Data Corpus 1030 (FIG. 1) or sentence “sillykitty jumps” illustrates its corresponding table of relationships orThird Table of Relations 1035 (FIG. 1). In this third table, the top rowassociates the word “silly,” the word “kitty” with their associationnumber “R17;” wherein R17 is the information responsible for identifyingthe association or relationship between “silly” and “kitty.” In similarfashion, the bottom row display the word “kitty,” the word “jumps” andtheir association number “M81.” As a result, each of the relationshipsformed/found in the third sentence is uniquely numbered or identified.Please note, in this particular example, the information identifyingeach of the associations is not in series but rather in random order orappearance. The Fourth Data Corpus 1040 (FIG. 1) is a sentence made ofgroup identifiers or “adj333 nou112 ver777” which in English spells thesentence “silly kitty jumps” along with its corresponding table ofrelationships or Fourth Table of Relations 1045 (FIG. 1). In this table,the top row associates the group identifiers adj333 (silly), nou112(kitty), with “6;” wherein “6” is the information identifying theirunique association. The bottom row associates another set of groupidentifiers or nou112 (kitty), ver777 (jumps) with number “12” which isthe information identifying their unique association. As a result, eachof the relationships found, formed or desired from the second sentenceis uniquely numbered, identified and/or differentiated. The Fifth DataCorpus 1050 (FIG. 1) is another sentence which this time is made ofeeggis or “adj33.1 nou11.4 ver77.1” which in English spells the sentence“silly kitty jumps.” The Fifth Table of Relations 1055 (FIG. 1)illustrates the associations that were found, formed or desired betweenthe eeggis of the said fifth sentence. In this table, the top rowassociates the eeggis adj33.1 (silly), nou11.4 (kitty), with theirassociation number “50.” The bottom row displays another associationbetween another group of eeggis or nou11.4 (kitty), ver77.1 (jumps) withnumber “18” which happens to be the information identifying their uniqueassociation. As a result, each eeggi association has its uniqueidentification number within the Fifth Data Corpus. Please note, in thisexample or table of relations, although the associations happened nextto each other, the information (numbers) identifying the saidassociations are not continuous or in series but are rather in randomorder.

FIG. 2A is a non-limiting exemplary diagram of some steps of theinventive method displaying an index table, here introduced asAssociative Index Table, which exploits the new idea of indexing theassociations between the different word elements of a given data corpus(at least two word elements are associated in the index table). Thisnovel type of table is not only indexing word elements, but is alsoindexing the associations the word elements experience in the differentdata corpuses. The set of Data Corpuses 2010 (FIG. 2A) comprises threeexemplary documents or sentences such as the first sentence or “[1] redboots and black hats,” the second sentence or “[2] black boots and redhats” and the third sentence or “[3] black hats and red boots.” Beneathit, is the Associative Index Table 2050 (FIG. 2A) displaying severalrows (1-8) and columns (Word1, Word2 and Page No.). In such fashion, theAssociative Index Table in every row associates two word elements inaddition of providing the information identifying their correspondingData Corpus or Page Number (Page No.). For example, in the AssociativeIndex Table 2050 (FIG. 2A) the seventh row illustrates or discloses thatthe word “red” experiences an association with the word “boots” and saidassociation is present or can be found in pages 1 and 3 ([1] and [3]).Another example, the eighth or last row, informs that the word “red”experiences an association with the word “hats” and that both words areassociated in page number 2 or [2].

FIG. 2B is another non-limiting exemplary diagram of some steps of theinventive method displaying another type of Associative Index Table,which in contrast to the index table from FIG. 2A, this type of tablealso uses the concept of sorting alphabetically the word elements fromeach association. The set of Data Corpuses 2010 (FIG. 2B) comprisesthree exemplary documents, pages or sentences such as the first sentenceor “[1] red boots and black hats,” the second sentence or “[2] blackboots and red hats” and the third sentence or “[3] black hats and redboots.” Beneath it, is the Associative Index Table 2051 (FIG. 2B) whichthis time displays or has four rows (1-4) and columns (Word1, Word2 andPage No.). Particular to this type of associative indexing table, theword elements of each association or row are arranged alphabeticallystarting with the first word element, or word element with the firstalphabetical order, and continuing with the second word element orelement with the last alphabetical order. In such fashion, every row ofthe Associative Index Table displays the word elements of every known oridentified association along with their location or Page Number (PageNo.). This kind of Associative Index Table, in comparison to theassociative index table of FIG. 2A, reduces or removes the need torepeat indices or words in the table, thus reducing its overall size.For example, the Associative Index Table 2051 (FIG. 2B) informs that inits third row, the word “boots” which experiences an association withthe word “red” (or vice versa) can be found in pages 1 and 3 ([1] and[3]). In such fashion, when a query or other requires the retrieval ofthe association of “red” with “boots,” the word element with the firstalphabetical order, in this case “boots,” can be used to find said wordelements and their association in the Associative Index Table. Anotherexample, the fourth or last row, the word “hats” and the word “red”experience an association in page number 2 or [2]. Please note, like inthis example that uses the method of alphabetically ordering the wordelements from first to last in the indexing table, there are a myriad ofparameters, arrangements and ordering preferences that can be utilizedfor creating, forming and/or arranging an associative index tablewithout ever departing from the main idea, scope and spirit of theinventive table and implementing methods.

FIG. 3A is a non-limiting exemplary block diagram of some significantsteps of the inventive method handling a query and an associative indextable for identifying information that matches the query and thecorresponding retrieval of data. The Query 3010 (FIG. 3A) comprises thephrase or sentence “red hats.” The Associative Procedure 3020 (FIG. 3A)such as CIRN (Conceptual Interrelating Network Protocol) identifies if arelationship is present or is possible between any of the words of thephrase or sentence “red hats.” Please note, there are a variety ofmethodologies or protocols such as different types of CIRN that areavailable for creating, forming, producing and/or identifyingassociations (desired or not) between the different word elements fromseveral kinds of data corpuses, such as those data corpuses using onlytext, words, group identifiers, eeggis, sounds, etc. In this example,the Query Table of Relations 3030 (FIG. 3A) illustrates that a singlerelationship is attained between the word elements “red” and “hats” ofthe Query. Consequentially, any such document(s) wherein the word “red”and “hats” are associated will represent a match. Next, the AssociativeIndex Table 2050 (FIG. 3A), similar to the index page discussed in FIG.2A, provides the information needed to allocate or find those pages ordata corpuses wherein “red” and “hats” are indeed related/associated asimplied by their row. For example, in the Associative Index Table, thefifth row indicates that “hats” and “black” both experience anassociation in pages 1 and 3 (in data corpuses [1] and [3]). In similarfashion, the last or eighth row of the Associative Index Table indicatesthat the word “red” and the word “hats” are related or experience andassociation in data corpus [2] or page 2. The Match Table 3070 (FIG. 3A)is a summary of the Associative Index Table with all the pages ordocuments that matched the query. For example, the Match Table indicatesthat the eighth row from the Associative Index Table produces a match tothe query, and that the same association between “red” and “hats” can befound in data corpus [2] or the second page. As a result, the seconddata corpus or second page is retrieved or displayed as indicated by theResults Display 3090 (FIG. 3A). Please note, the Match Table 3070 (FIG.3A) is used to illustrated the matches found and to aid or help theteaching of the present inventive method.

FIG. 3B is another non-limiting exemplary block diagram of somesignificant steps of the inventive method handling a query with severalassociations and an associative index table for identifying informationthat matches the associations of the query for retrieving matchinginformation. The Query 3010 (FIG. 3B) comprises the phrase or sentence“black hats and red boots.” The Associative Procedure 3020 (FIG. 3B)such as CIRN (Conceptual Interrelating Network Protocol) identifies if arelationship(s) is present or is possible between several groups ofwords from the phrase or sentence “black hats and red boots.” Pleasenote, there are a variety of methodologies or protocols such asdifferent types of CIRN that are available for creating, forming,producing and/or identifying associations (desired or not) between thedifferent word elements from several kinds of data corpuses, such asthose data corpuses using only text, words, group identifiers, eeggis,sounds, etc. In this particular example, the Query Table of Relations3030 (FIG. 3B) illustrates two different sets of relationships attainedfrom the word elements of the Query. For example, “black” associateswith “hats” and “red” associates with “boots.” Consequentially, theretrieval operation will involve any document(s) wherein the word“black” is associated with “hats” and wherein the word “red” isassociated with the word “boots.” Next, the Associative Index Table 2050(FIG. 3B) provides information that is needed to allocate or find thosepages (data corpuses) matching the query's elements and associations.For example, in the Associative Index Table, the fourth row indicatesthat “boots” and “red” are associated in pages 1 and 3. In similarfashion, the last or eighth row in the Index Table indicates that theword “red” and “hats” are associated in the data corpus [2] or page 2.The Query Table of Relations 3030 (FIG. 3B) specifically requires thattwo sets of associations need to be matched (“red—boots” and“black—hats”). Inspecting the Associative Index Table we can see thatthe first row has the first associative set of the query or “black” with“hats,” and that the seventh row has the second associative set or “red”with “boots.” As a result, the Match Table 3070 (FIG. 3B) illustratesboth set of matches, clearly identifying their pages, which in thisparticular case are in both cases pages 1 and 3. Consequentially, theResults Display 3090 (FIG. 3B) displays the matching records or pages[1] and [3]. Noteworthy, the Match Table 3070 (FIG. 3B) operates as anaid to help visualized the matching data obtained on the AssociativeIndex Table.

FIG. 3C is another non-limiting exemplary block diagram of a variationof some of the most significant steps the inventive method discussed inFIG. 3B this time manipulating other word elements such as groupidentifiers instead of words. In this example, the Query 3010 (FIG. 3C)comprises the group identifier sentence “aj88 no44+aj99 no33” which inEnglish spells or means “black hats and red boots.” The AssociativeProcedure 3020 (FIG. 3C) such as CIRN (Conceptual Interrelating NetworkProtocol) identifies any relationships present or possible betweenseveral groups of group identifiers from the phrase or sentence in theQuery. Please note, the CIRN protocols in this example are designed tohandle the involving group identifiers. The Query Table of Relations3030 (FIG. 3C) illustrates the resulting two relationships that werefound or obtained from the Query. For example, “aj88” (black) relates to“no44” (hats) while “aj99” (red) relates “no33” (boots). In suchfashion, any document(s) wherein “aj88” and “no44” relate and wherein“aj99” and “no33” relate too will represent a match of the query. TheAssociative Index Table 2050 (FIG. 3C) provides information needed toretrieve matching data. For example, in the Associative Index Table, thefifth row shows that “no44” and “aj88” are associated in pages [1] and[3]. Carefully inspecting the matches from the Query (“aj88-no44” and“aj99-no33”) in the Associative Index Tables we can see that the firstand the seventh rows have the same sets of group identifiersexperiencing the same relations as those found/formed in the Query. As aresult, the Match Table 3070 (FIG. 3C) illustrates the matching datafrom the Associative Index Table; wherein both associations aresimultaneously present in two different data corpuses or pages [1] and[3]. Consequentially, pages 1 and 3 are retrieved as indicated in theResults Display 3090 (FIG. 3C) displaying each page of group identifierswith their corresponding English parallel or translation.

FIG. 3D is another non-limiting exemplary block diagram of a variationof some of the most significant steps the inventive method discussed inFIG. 3B this time manipulating other word elements such as eeggisinstead of words. In this example, the Query 3010 (FIG. 3D) comprisesthe eeggi sentence “aj8.1 no4.0+aj9.5 no3.2” which in English spells ormeans “black hats and red boots.” The Spectrum Modifier 3015 (FIG. 3D),depending on synonym selection, modifies the eeggis of the query, suchas converting or reducing “aj8.1” to its root eeggi or spectrum “aj8.”which has no decimals. In such fashion, any synonym or eeggi in theaj8.xx region will be equally treated. The Associative Procedure 3020(FIG. 3D) such as CIRN (Conceptual Interrelating Network Protocol)identifies any relationships present or possible between several groupsof eeggis or eeggi regions from the phrase or sentence in the Query.Noteworthy, CIRN protocols in this example are designed to handle theinvolving word elements “eeggis.” In The Query Table of Relations 3030(FIG. 3D) illustrates the resulting two relationships that were found orobtained from the Query. For example, “aj8.” (black and synonyms ofblack) relates to “no4.0” (hats and synonyms of hats) while “aj9.5” (redand synonyms of red such as crimson) relates “no3.2” (boots and synonymsof boots). In such fashion, any document(s) wherein “aj8.” and “no4.”relate and wherein “aj9.” and “no3.” relate too, will represent a matchto the query. The Associative eeggi Index Table 2050 (FIG. 3D) providesinformation needed to retrieve matching data. For example, in theAssociative Index Table, the fifth row shows that eeggis “no4.” and“aj8.1” are associated in pages [1] and [3]. Carefully inspecting thematches from the Query (“aj8.-no4.” and “aj9.-no3.”) in the Associativeeeggi Index Tables we can see that the first and the seventh rows havethe same sets of eeggis or eeggi spectrums experiencing the samerelations/associations as those found or formed in the Query. As aresult, the Match Table 3070 (FIG. 3D) illustrates the matching datafrom the Associative eeggi Index Table; wherein both associations aresimultaneously present in two different data corpuses or pages [1] and[3]. Consequentially, pages 1 and 3 are retrieved as indicated in theResults Display 3090 (FIG. 3D) displaying each page of eeggis with theircorresponding English parallel or translation. Please note, pages [1]and [3] in the results involve the word “crimson” instead of “red” as inthe Query. This is because “crimson” (aj9.7) and “red” (aj9.5) bothshare the same eeggi spectrum or “aj9.”

FIG. 3E is another non-limiting exemplary block diagram of a variationof some of the most significant steps the inventive method, this timeimplementing an associative index table similar to that exemplified inFIG. 2B which uses alphabetically sorted indices. The Query 3010 (FIG.3E) comprises the phrase or sentence “red hats.” The AssociativeProcedure 3020 (FIG. 3E) such as CIRN (Conceptual Interrelating NetworkProtocol) identifies if a relationship(s) is present or possible betweenthe word elements or in this particular example, words, from the phraseor sentence “red hats.” Please note, there are a variety ofmethodologies or protocols such as different types of CIRN that areavailable for forming, producing and/or identifying associations(desired or not) between the different word elements from several kindsof data corpuses, such as those data corpuses using only text, words,group identifiers, eeggis, sounds, etc. As a result from saidassociative analysis, the Query Table of Relations 3030 (FIG. 3E)illustrates a relationship from the Query which happens to be betweenthe word “red” and “hats.” Next, the Sorting Procedure 3035 (FIG. 3E)arranges or alphabetically sorts, from first to last, the words of thedesired association, thus resulting in the Sorted Query 3040 (FIG. 3E).Please note, alphabetically speaking, “hats” is before “red,” thus thereason for the new arrangement. Consequentially, the retrieval operationwill involve any document(s) wherein the word “hats” is associated with“red.” Next, the Associative Index Table 2051 (FIG. 3E) provides theinformation needed to allocate or find the matching pages (datacorpuses) of the Sorted Query. For example, in the Associative IndexTable, the fourth or last row indicates that “hats” and “red” areassociated in page 2 or [2] and the third row indicates that “boots” and“red” are associated under data corpuses [1] and [3]. By carefullyinspecting the Associative Index Table we can see that indeed the fourthor last row has the same word elements and sorted associations as theSorted Query. As a result, the Match Table 3070 (FIG. 3E) aids toillustrate that it is in page 2 wherein the words “hats” and “red” canbe found being associated. Consequentially, the Results Display 3090(FIG. 3E) displays the record or page [2].

FIG. 3F is yet another non-limiting exemplary block diagram of somesignificant steps the inventive method handling a query with severalassociations and an associative index table for identifying informationthat matches the associations of the said query for retrieving matchinginformation. The Query 3010 (FIG. 3F) comprises the phrase or sentence“black hats and red boots.” The Associative Procedure 3020 (FIG. 3F)such as CIRN (Conceptual Interrelating Network Protocol) identifies if arelationship(s) is present or possible between several groups of wordsfrom the phrase or sentence “black hats and red boots.” Please note,there are a variety of methodologies or protocols such as differenttypes of CIRN that are available for forming, producing and/oridentifying associations (desired or not) between the different wordelements from several kinds of data corpuses, such as those datacorpuses using only text, words, group identifiers, eeggis, sounds, etc.In this particular example, the Query Table of Relations 3030 (FIG. 3F)illustrates the two different sets of relationships from the Query thatwere found, thanks to CIRN, between the different words. For example,“black” associates with “hats” and “red” associates with “boots.” Next,Sorting Procedure 3035 (FIG. 3F) arranges the elements accordingly toits sorting criteria, or as in this example, alphabetically. As aresult, the Sorted Query 3040 (FIG. 3F) illustrates the arranged sets ofassociations that are required for the retrieval of information. In suchfashion, the retrieval operation will involve any document(s) whereinthe word “black” is associated with “hats” and wherein the word “boots”is associated with the word “red.” Next, the Associative Index Table2051 (FIG. 3F) provides information that is needed to allocate or findthose pages (data corpuses) matching the query's word elements andcorresponding associations. For example, in the Associative Index Table,the fourth or last row indicates that “hats” and “red” are associated inpage number 2. Paying close attention to the Associative Index Table, wecan see that the first row has a first set identical to that of theSorted Query or “black” with “hats” in pages [1] and [3], and that thethird row has the second set or association between “boots” and “red”also in pages [1] and [3]. As a result, the Match Table 3070 (FIG. 3F)illustrates both set of matches, clearly identifying their pages 1 and3. Consequentially, both pages (1 and 3) have all the matching words andassociations between words. As a result, the Results Display 3090 (FIG.3B) displays the matching records or pages [1] and [3]. Noteworthy, theMatch Table 3070 (FIG. 3F) operates as an aid to help visualized thematching data obtained from the Associative Index Table andcorresponding page retrieval.

FIG. 3G is yet another non-limiting exemplary block diagram of avariation of some of the most significant steps the inventive methoddiscussed in FIG. 3B this time manipulating other word elements such asgroup identifiers instead of words. In this example, the Query 3010(FIG. 3G) comprises the group identifier sentence “aj88 no44+aj99 no33”which in English spells or means “black hats and red boots.” TheAssociative Procedure 3020 (FIG. 3G) such as CIRN (ConceptualInterrelating Network Protocol) identifies any relationships present orpossible between several groups of group identifiers from the phrase orsentence in the Query. Please note that the CIRN protocols in thisexample are designed to handle the involving group identifiers. TheQuery Table of Relations 3030 (FIG. 3G) illustrates the resulting tworelationships that were found or obtained from the Query. For example,“aj88” (black) relates to “no44” (hats) while “aj99” (red) relates“no33” (boots). Next, the Sorting Procedure 3035 (FIG. 3G), followingits particular sorting protocol, arranges and/or prepares the relationsof the Query Table of Relations to be properly identified in theAssociative Index Table of group identifiers. Please note, since thesorting protocol in this example exploits the format of sorting thegroup identifiers in descending alphabetical order, the order of thegroup identifiers in each association still remains unchanged. In suchfashion, any document(s) wherein “aj88” and “no44” relate and wherein“aj99” and “no33” relate do represent a match. The Associative IndexTable 2051 (FIG. 3G) provides information needed to retrieve anymatching data. For example, in the Associative Index Table, the fourthor last row shows that “aj99” and “no44” are associated in page number 2or [2]. Therefore, upon careful inspection of the matches from theSorted Query (“aj88-no44” and “aj99-no33”) in the Associative IndexTables we can see that the first and third rows have the same sets ofgroup identifiers experiencing the same relations. As a result, theMatch Table 3070 (FIG. 3G) illustrates the index information forretrieving matching data, which is this example, points to pages [1] and[3]. Consequentially, pages [1] and [5] are retrieved as indicated inthe Results Display 3090 (FIG. 3G) displaying each page of groupidentifiers with their corresponding English parallel or translations.

FIG. 3H is yet another non-limiting exemplary block diagram of avariation of some of the most significant steps the inventive methoddiscussed in FIG. 3B this time manipulating other word elements such aseeggis instead of words. In this example, the Query 3010 (FIG. 3H)comprises the eeggi sentence “aj8.1 no4.0+aj9.5 no3.2” which in Englishspells or means “black hats and red boots.” The Spectrum Modifier 3015(FIG. 3H) modifies the eeggis of the sentence into their correspondingeeggi spectrums. In such fashion, an eeggi such as “aj9.5” (red) isconverted to “aj9.” to also identify other synonyms such as “aj9.7”(crimson). The Associative Procedure 3020 (FIG. 3H) such as CIRN(Conceptual Interrelating Network Protocol) identifies any relationshipspresent or possible between several groups of group identifiers from thephrase or sentence in the Query. Please note the CIRN protocols in thisexample are designed to handle the involving eeggis. The Query Table ofRelations 3030 (FIG. 3H) illustrates the resulting two relationshipsthat were found or obtained from the Query. For example, “aj8.” (blackand synonyms of black) relates to “no4.” (hats and synonyms of hats)while “aj9.” (red and synonyms of red such as crimson) relates “no3.”(boots and synonyms of boots). Next, the Sorting Procedure 3035 (FIG.3G), following its particular sorting protocol, arranges and/or preparesthe relations of the Query Table of Relations to be properly identifiedin the Associative Index Table. Please note, since the sorting protocolin this example exploits the format of sorting the group identifiers indescending alphabetical order, the order of the eeggis in eachassociation still remains unchanged. In such fashion, any document(s)wherein “aj8.” and “no4.” relate and wherein “aj9.” and “no3.” relate dorepresent a match. The Associative Index Table 2051 (FIG. 3H)specifically designed to identified numerically sorted eeggis, providesthe information needed to retrieve any matching data. For example, inthe Associative Index Table, the fourth or last row shows that “aj9.5”(red) and “no4.0” (boots) associated in page number 2 or [2]. Therefore,upon careful inspection of the matches from the Sorted Query(“aj8.-no4.” and “aj9.-no3.”) on the Associative Index Table we can seethat the first and third rows have the same sets of eeggi spectrumsexperiencing the same relations/associations. As a result, the MatchTable 3070 (FIG. 3H) illustrates the index information for retrievingmatching data, which is this example, points to pages [1] and [3].Consequentially, pages [1] and are retrieved as indicated in the ResultsDisplay 3090 (FIG. 3H) displaying each page of eeggis with theircorresponding English parallel or translations. Please note, pages [1]and [3] in the results involve the word “crimson” instead of “red” as inthe Query. This is because “crimson” (aj9.7) and “red” (aj9.5) bothshare the same eeggi spectrum or “aj9.”

FIG. 3I is yet another non-limiting exemplary diagram of a variation ofsome of the most significant steps the inventive method this timeinvolving the word elements from associations in no particular order. Inother words, matches in the index tables need to have all words elementsof the associations regardless of order. In this example, the Query 3010(FIG. 3I) comprises the sentence “black hats and red boots.” TheAssociative Procedure 3020 (FIG. 3I) such as CIRN (ConceptualInterrelating Network Protocol) identifies any relationships present orpossible between several words from the phrase or sentence in the Query.The Query Table of Relations 3030 (FIG. 3I) illustrates the resultingtwo relationships that were found or obtained from the Query. Forexample, “black” relates/associates to “hats” while “red”relates/associates to “boots.” In such fashion, any document(s) wherein“black” and “hats” relate and wherein “red” and “boots” relate willrepresent a match of the query. The Associative Index Table 2050 (FIG.3I) provides information needed to retrieve any matching data. Forexample, in the Associative Index Table, the second row shows that“black” and “boots” are associated in page [2]. Then, upon carefulinspection of the words and associations from the Query in theAssociative Index Tables we can see that the first and the third rowshave the same sets of words experiencing the same relations among them.Please note that in this example, the order of the words in the queryand order of words in the index table are of no importance. In suchfashion, as long all words and association among said words from thequery can be found under the same index record (index record involvessame words) the documents identified by said index table are a match.Accordingly, the Match Table 3070 (FIG. 3I) illustrates the matchingdata from the Associative Index Table; wherein both associations aresimultaneously present in two different data corpuses or pages [1] and[3]. Consequentially, pages 1 and 3 are retrieved as indicated in theResults Display 3090 (FIG. 3I).

FIG. 3J is yet another non-limiting exemplary block diagram of avariation of some of the most significant steps the inventive methodimplementing a combination of index tables separated from the documentsown set of relations. In this example, the Query 3010 (FIG. 3J)comprises the English sentence “black boots and red hats.” TheAssociative Procedure 3020 (FIG. 3J) such as CIRN (ConceptualInterrelating Network Protocol) identifies any relationships present orpossible between several groups words from the phrase or sentence of theQuery. Please note the CIRN protocols in this example are designed tohandle the involving eeggis. The Query Table of Relations 3030 (FIG. 3J)illustrates the resulting two relationships that were found or obtainedfrom the Query. For example, “black” relates to “hats” while “red”relates to “boots.” Next, the Index Table 3050 (FIG. 3J) provides someof the information needed to find or retrieve any matching data. Forexample, in the Index Table, the fourth or last row shows that “boots”can be found in pages [1], [2] and [3], in such fashion, any queries ofthe word “boots” imply the retrieval of pages [1], [2] and [3].Therefore, upon careful inspection of the word of the Query Table ofRelations, we can see that pages [1], [2] and [3] have all the wordsneeded. Next, the Source of Data 3070 (FIG. 3J) depicts every documentor page in addition to the associations that each page has. For example,the First Page File 3071 (FIG. 3J) in the Source of Data, displays itscontent “red boots and black hats” along with the associations (if any)that any of its words experiences. In this First Page File, “red” isassociated to “boots” while “black” is associated to “hats.” The SecondPage File 3072 (FIG. 3J) displays its page content or “black boots andred hats” along with the associations the words of the page have; whichin this example involves “black” having a relation with “boots” and“red” having a relation with “hats.” Finally, the Third Page File 3073(FIG. 3J) displays its page content or “black hats and red boots” withtheir corresponding associations; wherein “black” associates with “hats”and “red” associates with “boots.” Consequentially, inspecting theassociations and words of the Query (or Query table of Relations) withthe words of the pages in the source of Data that experience the samewords and associations, we can see that only page [2] (The Second PageFile) offers identical words and associations among them. As a result,the Results Display 3090 (FIG. 3J) displays the only matching page [2]or “black boots and red hats.”

FIG. 4A is a non-limiting block diagram of some of the steps of theinventive method discussed in FIG. 1 and FIG. 2A for producing orproviding an indexing table for finding and/or comparing and/orretrieving matching information. The First Step 4010 (FIG. 4A) involvesthe step of identifying a First Word Element (a word element is aninformation identifying at least one of a: word, concept, idea, meaning,image and grammatical element) in a Data Corpus. For example, in a datacorpus such as a query with three word elements, one of the wordelements is identified or selected. The Second Step 4020 (FIG. 4A)involves the step of identifying another or Second Word Element in thesaid Data Corpus. For example, from the query in the First Step whichidentified one element, in this second step another word element fromthe remaining two elements is identified or selected. The next or ThirdStep 4030 (FIG. 4A) involves the step of identifying and/or finding anassociation between said First Word Element and said Second WordElements through the use of an associative protocol such as CIRN. Forexample, CIRN (Conceptual Inter-relating Network Protocols) identifiesand/or forms associations between different types of word elements of aparticular data corpus. In such fashion, a sentence such as “fat catsran” when analyzed by CIRN will find or form associations between “fat”and “cats” and also find or form another association between “cats” and“ran.” The next of Fourth Step 4040 (FIG. 4A) involves the obvious stepof associating the First Word Element, Second Word Element andinformation identifying the data corpus wherein the association betweenboth elements occurs. For example, in the sentence or data corpus “fatcats ran” two associations are identified. As a result, each of theassociations is formed including the information identifying theircorresponding data corpus. The next or Fifth Step 4050 (FIG. 4A)involves the next obvious step of registering all the informationnecessary for effectively identifying the word elements, theirassociation and their data corpus where they are found. For example, onindexing tables of the current art, every word in its index table has aninformation identifying its source document(s) or page(s) (where theword is found). In such fashion, search engines can quickly retrievethose documents comprising the word of the query. However, in thisdisclosed inventive index table, at least two words experiencing anyparticular association are used along with the information foridentifying the documents or pages containing them (document comprisingboth words been associated).

FIG. 4B is a non-limiting block diagram of some of the steps of theinventive method discussed in FIG. 1 and FIG. 2B for producing orproviding an indexing table for finding and/or comparing and/orretrieving matching information. The First Step 4010 (FIG. 4B) involvesthe step of identifying a First Word Element (a word element is aninformation identifying at least one of a: word, concept, idea, meaning,image and grammatical element) in a Data Corpus. For example, in a datacorpus such as a query with four word elements, one of the word elementsis identified or selected. The Second Step 4020 (FIG. 4B) involves thestep of identifying another or Second Word Element in the said DataCorpus. For example, from the query in the First Step which identifiedone element, in this second step another word element from the remainingthree elements is identified or selected. The next or Third Step 4030(FIG. 4B) involves the step of identifying and/or finding an associationbetween said First Word Element and said Second Word Elements throughthe use of an associative protocol such as CIRN. For example, CIRN(Conceptual Inter-relating Network Protocols) identifies and/or formsassociations between different types of word elements of a particulardata corpus. In such fashion, a sentence such as “fat cats and sillydogs” when analyzed by CIRN will find or form associations between “fat”and “cats” and also find or form another association between “silly” and“dogs.” The next of Fourth Step 4040 (FIG. 4B) involves the obvious stepof associating the First Word Elements, Second Word Element andinformation identifying the data corpus wherein the association occurs.For example, in the sentence or data corpus “fat cats and silly dogs”two associations are identified. As a result, each of the associationsis formed including the information identifying their corresponding datacorpus. The next or Fifth Step 4050 (FIG. 4B) involves the step ofsorting or arranging the word elements of the found or desiredassociations into a particular order or particular sequence. Forexample, in an association such as “fat” and “cats,” sorting theelements in ascending alphabetical order, results in placing the word“cats” first and the word “fat” as second. The next or Sixth Step 4060(FIG. 4B) involves the obvious step of registering the informationnecessary for effectively forming the Associative Index'Table such asidentifying the word elements, their association and their data corpuswhere they are found. For example, in indexing tables of the currentart, every word in its index table has or uses an information foridentifying the document(s) or page(s) wherein the word is present. Insuch fashion, search engines can quickly retrieve those documentscomprising the word of the query. However, in this disclosed AssociativeIndex Table, at least two words experiencing any particular associationare sorted and registered along with the information for identifying thedocuments or pages wherein at least said both words are indeed present

FIG. 5A is a non-limiting block diagram of some of the main steps of theinventive method displayed in FIGS. 3A, 3B, 3C and 3D for retrievinginformation. The First Step 5010 (FIG. 5A) involves the step ofidentifying an association between a plurality of word elements from afirst data corpus such as a query. For example, after analyzing a querysuch as “red hats” it is found that an association exists between thewords “red” and “hats.” The next or Second Step 5020 (FIG. 5A) involvesthe step of identifying a first word element from said first data corpusor said association. For example, from the query “red hats” the word“red” is identified or selected. The next or Third Step 5030 (FIG. 5A)involves the step of searching an index table comprising at least one ofa: said first word element, information identifying a data corpuscomprising said first word element, and information identifying a secondword element from said association. For example, from the query “redhats,” the word “red” is searched and/or found in an index table alsoidentifying that word “hats” and also identifying at least one documentwherein both “red” and “hats” are associated. In such fashion, lookingup for the word “red” or “hats” on the index table, it providesinformation wherein the set of words are associated. The next or FourthStep 5040 (FIG. 5A) involves the obvious step of identifying a match insaid index table. For example, looking up on the index table, a match ofassociations between “red” and “hats” is found identical to that of“red” and “hats” in the query. The next or Fifth Step 5050 (FIG. 5)involves the next obvious step of identifying a second data corpusidentified by said identified match from said index table. For example,the index table identifies documents X and Y to have identicalassociations between the same elements as the query. The final or SixthStep 5060 (FIG. 5A) involves the step of retrieving the said identifiedsecond data corpus. For example, displaying or retrieving document X andY as a match to the word elements of the query.

FIG. 5B is yet another non-limiting block diagram of some of the mainsteps of the inventive method displayed in FIGS. 3E, 3F, 3G and 3H forretrieving information. The First Step 5010 (FIG. 5B) involves the stepof identifying an association between a plurality of word elements froma first data corpus such as a query. For example, after analyzing aquery such as “red hats” it is found that an association exists or isbeing identified between the words “red” and “hats.” The next or SecondStep 5020 (FIG. 5B) involves the step sorting the word elements fromsaid association. For example, the word associated word elements “red”and “hats” are sorted or arranged in a particular and/or desired ordersuch as arranging them in alphabetical descending order. As a result,the word “hats” is first or before the word “red” since “red” beginswith the letter “r.” The next or Third Step 5030 (FIG. 5B) involves thesimple step of identifying a word from said sorting event. For example,selecting the first word of the sorting process. In such fashion, anindex table can be search for finding the elements of the query. Thenext or Fourth Step 5040 (FIG. 5B) involves the step of searching anindex table comprising at least one of a: said first word element,information identifying a data corpus comprising said first wordelement, and information identifying a second word element from saidassociation. For example, from the query “red hats,” the word “red” issearched and/or found in an index table also identifying that word“hats” and also identifying at least one document wherein said both“red” and “hats” are associated. In such fashion, looking up for theword “red” or “hats” on the index table, it provides information whereinthe set of words are associated. The next or Fifth Step 5050 (FIG. 5B)involves the obvious step of identifying a match in said index table.For example, looking up on the index table, a match of the query'sassociations between “red” and “hats” is found. The next or Sixth Step5060 (FIG. 5B) involves the next obvious step of identifying a seconddata corpus identified by said identified index table. For example,identifying documents X and Y of having the same associations as thequery. The final or Seventh Step 5070 (FIG. 5B) involves the obviousstep of retrieving the said second data corpus. For example, displaying,producing or retrieving documents X and Y as a resulting match to thequery.

FIG. 5C is yet another non-limiting block diagram of some of the mainsteps of the inventive method displayed in FIG. 3J for retrievinginformation. The First Step 5010 (FIG. 5C) involves the step ofidentifying an association between a plurality of word elements from afirst data corpus such as a query. For example, after analyzing a querysuch as “red hats” it is found that an association exists or is beingidentified between the words “red” and “hats.” The next or Second Step5020 (FIG. 5B) involves the step of sorting the word elements from saidassociation. For example, the word associated word elements “red” and“hats” are sorted or arranged in a particular and/or desired order suchas arranging them in alphabetical descending order. As a result, theword “hats” is first or before the word “red” since “red” begins withthe letter “r.” The next or Third Step 5030 (FIG. 5B) involves thesimple step of identifying a word from said sorting event. For example,selecting the first word of the sorting process. In such fashion, anindex table can be search for finding the elements of the query. Thenext or Fourth Step 5040 (FIG. 5B) involves the step of searching anindex table comprising at least one of a: said first word element,information identifying a data corpus comprising said first wordelement, and information identifying a second word element from saidassociation. For example, from the query “red hats,” the word “red” issearched and/or found in an index table also identifying that word“hats” and also identifying at least one document wherein said both“red” and “hats” are associated. In such fashion, looking up for theword “red” or “hats” on the index table, it provides information whereinthe set of words are associated. The next or Fifth Step 5050 (FIG. 5B)involves the obvious step of identifying a match in said index table.For example, looking up on the index table, a match of the query'sassociations between “red” and “hats” is found. The next or Sixth Step5060 (FIG. 5B) involves the next obvious step of identifying a seconddata corpus identified by said identified index table. For example,identifying documents X and Y of having the same associations as thequery. The final or Seventh Step 5070 (FIG. 5B) involves the obviousstep of retrieving the said second data corpus. For example, displaying,producing or retrieving documents X and Y as a resulting match to thequery.

FIG. 6 is a non-limiting block diagram of the some steps of theinventive method exploring a more general view of the steps mentioned inFIG. 5A and FIG. 5B. The First Step 6010 (FIG. 6) involves the step ofidentifying an association between a plurality of word elements from afirst data corpus such as a query. For example, finding an associationbetween at least two elements in a query. The next or Second Step 6020(FIG. 6), involves the step of searching an index table identifying saidplurality of word elements being associated. The next or Third Step 6030(FIG. 6) involves the step of identifying a match in the said indextable. The next or Fourth Step 6040 (FIG. 6) involves the step ofidentifying a second data corpus identified by said match. For the indextable indicates that documents X and Y have the word elements inidentical or similar associations as those of the query. The final orFifth Step 6050 (FIG. 6) involves the obvious step of retrieving thesecond data corpus. For example, this final step involves the retrieval,display and/or providing procedure of documents X and Y as results tothe query.

The enablements described in detail above are considered novel over theprior art of record and are considered critical to the operation of atleast one aspect of an apparatus and its method of use and to theachievement of the above described objectives. The words used in thisspecification to describe the instant embodiments are to be understoodnot only in the sense of their commonly defined meanings, but to includeby special definition in this specification: structure, material or actsbeyond the scope of the commonly defined meanings. Thus if an elementcan be understood in the context of this specification as including morethan one meaning, then its use must be understood as being generic toall possible meanings supported by the specification and by the word orwords describing the element.

The definitions of the words or drawing elements described herein aremeant to include not only the combination of elements which areliterally set forth, but all equivalent structure, material or acts forperforming substantially the same function in substantially the same wayto obtain substantially the same result. In this sense it is thereforecontemplated that an equivalent substitution of two or more elements maybe made for any one of the elements described and its variousembodiments or that a single element may be substituted for two or moreelements in a claim.

Changes from the claimed subject matter as viewed by a person withordinary skill in the art, now known or later devised, are expresslycontemplated as being equivalents within the scope intended and itsvarious embodiments. Therefore, obvious substitutions now or later knownto one with ordinary skill in the art are defined to be within the scopeof the defined elements. This disclosure is thus meant to be understoodto include what is specifically illustrated and described above, what isconceptually equivalent, what can be obviously substituted, and alsowhat incorporates the essential ideas.

The scope of this description is to be interpreted only in conjunctionwith the appended claims and it is made clear, here, that each namedinventor believes that the claimed subject matter is what is intended tobe patented.

CONCLUSION

From the foregoing, a series of novel methods for forming and indextable, implementing an indexing methodology and method for retrievinginformation can be appreciated. The described methods overcomes thelimitations encountered by current information technologies such assearch engines, speech recognition, word processors, and others whichfail to identify and/or effectively implement the underlyingassociations between different kinds of word elements; which potentiallyleads to the generation of irrelevant data, irrational data, randomlyisolated words and user confusion, to allow current and futureinformation technologies to properly and effectively manipulate,identify, select, match and retrieve data.

1. A Method for indexing information comprising the steps of: a)Identifying a first word element such as an information identifying aword, concept, idea, meaning, image and grammatical information in afirst data corpus b) Identifying a second word element such as aninformation identifying a word, concept, idea, meaning, image andgrammatical information in said first data corpus c) Identifying a firstassociation between said first word element and said second word elementimplementing an associative protocol such as CIRN d) Implementing afirst information for identifying said first association e) Implementinga second information for identifying said first data corpus f)Registering at least one of a said: first information and secondinformation with at least one of a said: first word element and secondword element
 2. A method for retrieving information comprising the stepsof: a) Identifying an association between one of a plurality of wordelements from a first data corpus such as a query b) Identifying a wordelement from said plurality c) Searching an index table comprising atleast one of a: said first word element, information identifying a datacorpus of said first word element and information identifying anassociation of said first word element d) Searching an index tablecomprising at least one of a: said second word element, informationidentifying a data corpus of said second word element and informationidentifying an association of said second word element e) Identifying aninformation identifying a data corpus; wherein said first word elementand said second word element have the same said information identifyingan association f) Retrieving said data corpus
 3. A method for providingand index table comprising the steps of: a) Identifying an index tableb) Adding an information field to said index table for containinginformation for identifying at least one information identifying andassociation between its indexed word element with one other wordelement. c) Registering information in said information field
 4. Amethod for identifying information of an index table in a data corpussuch as a query, the method comprising the steps of: a) Identifying afirst group of word elements in a data corpus such as a query, b)Identifying a first association between said first group of wordelements, c) Identifying a second group of word elements in said datacorpus, d) Identifying a second association between said second group ofword elements, e) Assigning each said association a unique identifyinginformation.